Although the present invention can be applied generally in the field of image matching, the invention has particular utility in the area of personal identification. Identification of an individual or verifying whether an individual is the person he claims to be is a common problem faced by individuals, businesses, and governments. While sophisticated personal identification is often used for security in sensitive government and commercial installations, matching for personal identification has potential application wherever a person's identity needs to be identified or verified, such as in the control of access to physical locations, such as airports, industrial locations and in the home. It also has potential application in the control of access to computing and data management equipment and in banking or commercial transactions.
Methods for identification of an individual often include reliance upon knowledge of restricted information, such as a password, possession of a restricted article, such as a passkey, or physical appearance, such as matching a reference photo. Biometric indicia have also been used for personal identification. Biometrics is the study of biological phenomena, and in the area of personal identification, some chosen characteristic of a person is used to identify or verify that person's identity. Biometric identification is particularly useful because certain personal characteristics are substantially unique to each person and are difficult to reproduce by an impostor. Further, the recording and analysis of biometric data can be automated, thereby allowing use of computer controlled electronics and digital recording techniques.
The use of biometric indicia for identification purposes requires that a particular biometric factor is substantially unique for each individual, that it is readily measured and that it is invariant over the time period during which the person may be tested for identification. Further, the biometric indicia should be difficult to duplicate by an impostor in order to secure against erroneous identification. The biometric indicia may be biologically determined or it may be some characteristic that is learned or acquired, such as handwriting or voice patterns.
Some of the biometric characteristics most investigated today for use in a personal identification system include fingerprints, hand or palm prints, retina scans, signatures and voice patterns. Hand or palm print techniques typically evaluate the shape of a person's hand or other significant features such as creases in the palm, but these techniques may be fooled by templates or models of the hand of an authorized person. Retina scanning techniques evaluate the pattern of blood vessels in a person's retina. A drawback of this technique is that the blood vessel pattern may vary over time, such as when alcohol is in the blood stream or during irregular use of glasses or contact lenses. Also, a user may feel uneasy about having his or her eye illuminated for retina scanning or the possibility of eye contamination if there is contact between the eye and the scanning apparatus. Signatures may be easily forged and must usually be evaluated by a human operator, although research has been done on automated systems that evaluate the dynamics of a person's handwriting, such as the speed and the force of hand movement and pauses in writing. Using voice patterns as the identifying characteristic encounters difficulties owing to the wide variations in a person's voice over time, the presence of background noise during an evaluation and the potential for an impostor to fool the system with a recording of the voice of an authorized person.
Although many biologically determined indicia have been used over the years, such as the human eye, facial features, bone structure, fingernail pattern and creases in the palm or fingers of the hand, fingerprints have been the most commonly used biometric characteristic for personal identification. The technology of personal identification through fingerprint analysis has long been used in law enforcement. This long term experience with fingerprint analysis has developed a large amount of information about fingerprints and has confirmed the uniqueness of a person's fingerprints. Historically, in law enforcement, fingerprints have been recorded by inking the fingerprint and making a print on a card for storage. Particularly for applications outside of law enforcement, less burdensome and intrusive recording methods needed to be developed.
Various electro-mechanical systems for recording and matching a live fingerprint with a stored representation of the fingerprint of the authorized person have been developed. In one type of system, an image of the live fingerprint pattern is read and optically compared with a master fingerprint that was stored on film. Difficulties arise in this system in aligning the live and the master fingerprint patterns, leading to the use of complicated devices to secure the user's finger in exact alignment with the recording device or to rotate and translate the live pattern with respect to the stored pattern to perform the registration. Further, because this type of system relies on a precise one-to-one sizing of the live and stored fingerprint patterns, errors in matching can occur where the live fingerprint pattern is deformed even slightly, such as when the finger is swollen or is pressed hard against the reading surface.
In another type of fingerprint matching system, the live fingerprint is read and the image is compared with a hologram of the fingerprint of the authorized person. This system requires the storage of a library of holograms at the testing location for each authorized person in the system and the use of a specialized light source and a complicated optics system.
The trend in automatic fingerprint matching is toward the increased use of electronics and computer control of the matching process, while minimizing the reliance on moving mechanical parts and complicated optics systems. In such a system, the live fingerprint typically is scanned and digitally recorded as a binary image of the fingerprint pattern. Characteristic features of the fingerprint pattern, such as ridge endings, points of ridge bifurcation and the core of a whorl, each defining a feature of the fingerprint pattern, are found in the binary fingerprint image. The binary fingerprint image is compared with a stored master pattern that has been derived previously from a fingerprint of the authorized person in order to determine whether there is a match. Many systems that attempt to identify features in the fingerprint pattern such as forks or ridge endings must make decisions of matching early in the comparison process. If any errors are made early in the decision making process, such errors propagate through the remainder of the decision making process, thereby increasing the chance of error. Also, many systems have preconceived notions of what features should be recognized in the fingerprint image. For example, based on human studies of the fingerprint, certain categories of fingerprints have been identified and features in fingerprints named. Identifying such predetermined features and phenomena has been integral in most fingerprint identification systems.
In the simplest of fingerprint matching systems, the features of both the live and the master fingerprints are compared and a correlation function is applied to the comparison. A significant disadvantage of this type of system is that the user's finger typically is required to be in exact alignment with the image recording device so that the coordinate system of the binary image derived from the live fingerprint pattern is in the same orientation and position as the coordinate system on which the master fingerprint pattern. Further, as human skin is elastic, how the skin of the finger interacts with the platen of the image recording device also can change the recorded live fingerprint. For example, elastic deformations or distortions of the recorded fingerprint due to the elastic nature of skin may be recorded, or oil on the skin may cause the platen to record wider ridges. Such variables often defeat the accuracy of systems that rely on correlation functions.